Glossary · Agentic
Agentic AI
Agentic AI is software that uses a language model to plan and carry out multi-step tasks by calling tools, observing the results, and choosing its next action in a loop.
Agentic AI is software that uses a language model to plan and run multi-step tasks by calling tools, observing results, and choosing its next action in a loop with memory and stopping conditions. It differs from a single prompt-and-response. Gartner expects over 40% of agentic AI projects to be cancelled by end-2027.
- An agent runs a plan-act-observe loop with tools, memory and stopping conditions, not a single reply.
- Gartner, June 2025: over 40% of agentic AI projects will be cancelled by the end of 2027.
- Gartner estimates only about 130 of thousands of self-described agentic vendors are genuine.
- The hard part is orchestration and evaluation, not the model: that is where projects fail.
- Autonomy is a dial, not a switch: most enterprise value sits at partial autonomy with human checkpoints.
Also known as: AI agent, agentic system
Agentic AI is software that uses a language model to plan and carry out multi-step tasks by calling tools, observing the results, and choosing its next action.
Where a chatbot returns one answer, an agent is given a goal and runs a loop: it plans, acts through tools or APIs, reads what happened, and decides the next step until the task completes or a stopping condition stops it. Memory carries context across steps.
How agentic AI works
An agent has four moving parts. A model does the reasoning and decides what to do. Tools let it act: call an API, query a database, run code, send a message. A loop feeds the result of each action back in so the next decision is informed by the last. And stopping conditions (a completed goal, a step budget, a human checkpoint) stop it running forever.
The reasoning pattern most systems use is plan, act, observe, repeat. The model proposes a step, the orchestration layer executes it through a tool, the result is returned, and the model decides whether the goal is met. This is what separates agent orchestration as a discipline from simply prompting a model: the coordination, state and retries are where reliability is won or lost.
Why agentic AI matters for enterprise AI adoption
Agentic AI is the layer where the free tier stops. Microsoft, AWS, IBM and nasscom FutureSkills teach prompting to millions; almost none teach agent orchestration, tool use, evaluation or governance. That is exactly where enterprise value and enterprise failure both live.
It is also where the money is being lost. Gartner predicted in June 2025 that over 40% of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls (Gartner, 25 June 2025). It also estimated that only around 130 of the thousands of vendors calling themselves agentic are genuine, describing the rest as "agent washing". For a transformation owner, the practical consequence is that the word on a slide tells you nothing; the workflow, the baseline and the evaluation harness tell you everything.
Common mistakes with agentic AI
The first mistake is treating autonomy as a switch rather than a dial. Full autonomy is rarely the right setting for a first project; partial autonomy with a human in the loop at the expensive step captures most of the value at a fraction of the risk.
The second is building the agent before deciding how you will know it works. Without an evaluation harness, a team cannot tell a genuine regression from run-to-run variance, and the project drifts. The third is choosing a workflow because it is visible rather than because it is measurable: MIT found more than half of GenAI budgets went to sales and marketing despite better returns in the back office.
Related terms
- Agent orchestration: the coordination layer that makes a multi-agent task reliable.
- Tool use: how an agent calls external functions and data during a task.
- Human in the loop: the review checkpoints that keep autonomy safe.
- Evaluation harness: how you prove an agent still works after a change.
- Workflow absorption: measuring whether the agent actually changed the work.
How Chokmah approaches agentic AI
We sell agentic outcomes, not agentic vocabulary. Before an engagement starts we name the workflow, and we tell you which workflows we would refuse to automate, because a vendor that cannot name what to leave alone is selling the thing that fails most of the time. Every workflow sprint ships a working agent and the evaluation harness that proves it, and the client owns the code. Autonomy is set deliberately, with a human checkpoint wherever an error would be expensive and hard to reverse.
Sources
- Gartner, Over 40% of Agentic AI Projects Will Be Canceled by End of 2027, 25 June 2025. https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027
- MIT NANDA, The GenAI Divide: State of AI in Business 2025, July 2025. https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf
Related terms
- Agent orchestrationAgent orchestration is the coordination layer that routes work across multiple AI agents and tools so a multi-step task completes reliably, handling state, retries, handoffs and stopping conditions.
- Tool useTool use is the mechanism by which an AI model calls external functions, APIs or data sources during a task, instead of answering only from what it learned in training.
- Human in the loopHuman in the loop is a workflow design in which a person reviews, approves or corrects an AI system's output at defined checkpoints before it takes effect, keeping accountability with a human.
- Evaluation harnessAn agent evaluation harness is a repeatable test suite that scores an AI agent's outputs against fixed, versioned cases before and after every change, so teams can tell regression from variance.
- Workflow absorptionWorkflow absorption measures whether AI has actually changed how work runs (steps redesigned, cycle time reduced, errors cut) as opposed to adoption, which only counts access such as seats and logins.
Frequently asked questions
A chatbot answers one message at a time from its training data and any context you paste in. An agentic system is given a goal, then plans a sequence of steps, calls tools or APIs to act in the world, reads the results, and decides what to do next until the task is done or a stopping condition is hit. The chatbot responds; the agent pursues an objective.
A model plus tools is the engine, not the whole system. Agentic AI adds the parts that make it reliable: an orchestration layer that manages state and retries, memory that carries context across steps, evaluation that scores outputs against fixed cases, and human checkpoints where errors are expensive. Skipping those parts is why most agentic pilots stall before production.
Gartner attributes the 40%-plus cancellation rate it expects by 2027 to escalating costs, unclear business value, and inadequate risk controls, not to weak models. In practice, projects fail when nobody named the workflow before starting, no baseline was taken, and no evaluation harness existed to tell a regression apart from normal variance. These are organisational gaps, not technical ones.
No. Agentic autonomy suits high-frequency, rule-dense, measurable workflows where an error is visible and recoverable. It is a poor fit where errors are expensive and invisible, where rules change faster than you can re-evaluate, or where regulatory accountability cannot be delegated. Deciding which workflows to leave alone is part of the work, not a failure of it.
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